Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 11 Dec 2012 05:20:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/11/t1355221233xmnmq6hsa09zi9w.htm/, Retrieved Thu, 28 Mar 2024 17:26:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198392, Retrieved Thu, 28 Mar 2024 17:26:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [Workshop 10 - Lea...] [2012-12-06 16:13:05] [d7df2128d8ee42e4ceceb756b40e5412]
- R P     [Recursive Partitioning (Regression Trees)] [] [2012-12-11 10:20:14] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1	1	41	38	13	12	14
1	1	39	32	16	11	18
1	1	30	35	19	15	11
1	0	31	33	15	6	12
1	1	34	37	14	13	16
1	1	35	29	13	10	18
1	1	39	31	19	12	14
1	1	34	36	15	14	14
1	1	36	35	14	12	15
1	1	37	38	15	9	15
1	0	38	31	16	10	17
1	1	36	34	16	12	19
1	0	38	35	16	12	10
1	1	39	38	16	11	16
1	1	33	37	17	15	18
1	0	32	33	15	12	14
1	0	36	32	15	10	14
1	1	38	38	20	12	17
1	0	39	38	18	11	14
1	1	32	32	16	12	16
1	0	32	33	16	11	18
1	1	31	31	16	12	11
1	1	39	38	19	13	14
1	1	37	39	16	11	12
1	0	39	32	17	12	17
1	1	41	32	17	13	9
1	0	36	35	16	10	16
1	1	33	37	15	14	14
1	1	33	33	16	12	15
1	0	34	33	14	10	11
1	1	31	31	15	12	16
1	0	27	32	12	8	13
1	1	37	31	14	10	17
1	1	34	37	16	12	15
1	0	34	30	14	12	14
1	0	32	33	10	7	16
1	0	29	31	10	9	9
1	0	36	33	14	12	15
1	1	29	31	16	10	17
1	0	35	33	16	10	13
1	0	37	32	16	10	15
1	1	34	33	14	12	16
1	0	38	32	20	15	16
1	0	35	33	14	10	12
1	1	38	28	14	10	15
1	1	37	35	11	12	11
1	1	38	39	14	13	15
1	1	33	34	15	11	15
1	1	36	38	16	11	17
1	0	38	32	14	12	13
1	1	32	38	16	14	16
1	0	32	30	14	10	14
1	0	32	33	12	12	11
1	1	34	38	16	13	12
1	0	32	32	9	5	12
1	1	37	35	14	6	15
1	1	39	34	16	12	16
1	1	29	34	16	12	15
1	0	37	36	15	11	12
1	1	35	34	16	10	12
1	0	30	28	12	7	8
1	0	38	34	16	12	13
1	1	34	35	16	14	11
1	1	31	35	14	11	14
1	1	34	31	16	12	15
1	0	35	37	17	13	10
1	1	36	35	18	14	11
1	0	30	27	18	11	12
1	1	39	40	12	12	15
1	0	35	37	16	12	15
1	0	38	36	10	8	14
1	1	31	38	14	11	16
1	1	34	39	18	14	15
1	0	38	41	18	14	15
1	0	34	27	16	12	13
1	1	39	30	17	9	12
1	1	37	37	16	13	17
1	1	34	31	16	11	13
1	0	28	31	13	12	15
1	0	37	27	16	12	13
1	0	33	36	16	12	15
1	1	35	37	16	12	15
1	0	37	33	15	12	16
1	1	32	34	15	11	15
1	1	33	31	16	10	14
1	0	38	39	14	9	15
1	1	33	34	16	12	14
1	1	29	32	16	12	13
1	1	33	33	15	12	7
1	1	31	36	12	9	17
1	1	36	32	17	15	13
1	1	35	41	16	12	15
1	1	32	28	15	12	14
1	1	29	30	13	12	13
1	1	39	36	16	10	16
1	1	37	35	16	13	12
1	1	35	31	16	9	14
1	0	37	34	16	12	17
1	0	32	36	14	10	15
1	1	38	36	16	14	17
1	0	37	35	16	11	12
1	1	36	37	20	15	16
1	0	32	28	15	11	11
1	1	33	39	16	11	15
1	0	40	32	13	12	9
1	1	38	35	17	12	16
1	0	41	39	16	12	15
1	0	36	35	16	11	10
1	1	43	42	12	7	10
1	1	30	34	16	12	15
1	1	31	33	16	14	11
1	1	32	41	17	11	13
1	1	37	34	12	10	18
1	0	37	32	18	13	16
1	1	33	40	14	13	14
1	1	34	40	14	8	14
1	1	33	35	13	11	14
1	1	38	36	16	12	14
1	0	33	37	13	11	12
1	1	31	27	16	13	14
1	1	38	39	13	12	15
1	1	37	38	16	14	15
1	1	36	31	15	13	15
1	1	31	33	16	15	13
1	0	39	32	15	10	17
1	1	44	39	17	11	17
1	1	33	36	15	9	19
1	1	35	33	12	11	15
1	0	32	33	16	10	13
1	0	28	32	10	11	9
1	1	40	37	16	8	15
1	0	27	30	12	11	15
1	0	37	38	14	12	15
1	1	32	29	15	12	16
1	0	28	22	13	9	11
1	0	34	35	15	11	14
1	1	30	35	11	10	11
1	1	35	34	12	8	15
1	0	31	35	11	9	13
1	1	32	34	16	8	15
1	0	30	37	15	9	16
1	1	30	35	17	15	14
1	0	31	23	16	11	15
1	1	40	31	10	8	16
1	1	32	27	18	13	16
1	0	36	36	13	12	11
1	0	32	31	16	12	12
1	0	35	32	13	9	9
1	1	38	39	10	7	16
1	1	42	37	15	13	13
1	0	34	38	16	9	16
1	1	35	39	16	6	12
1	1	38	34	14	8	9
1	1	33	31	10	8	13
1	1	32	37	13	6	14
1	1	33	36	15	9	19
1	1	34	32	16	11	13
1	1	32	38	12	8	12
0	0	27	26	13	10	10
0	0	31	26	12	8	14
0	0	38	33	17	14	16
0	1	34	39	15	10	10
0	0	24	30	10	8	11
0	0	30	33	14	11	14
0	1	26	25	11	12	12
0	1	34	38	13	12	9
0	0	27	37	16	12	9
0	0	37	31	12	5	11
0	1	36	37	16	12	16
0	0	41	35	12	10	9
0	1	29	25	9	7	13
0	1	36	28	12	12	16
0	0	32	35	15	11	13
0	1	37	33	12	8	9
0	0	30	30	12	9	12
0	1	31	31	14	10	16
0	1	38	37	12	9	11
0	1	36	36	16	12	14
0	0	35	30	11	6	13
0	0	31	36	19	15	15
0	0	38	32	15	12	14
0	1	22	28	8	12	16
0	1	32	36	16	12	13
0	0	36	34	17	11	14
0	1	39	31	12	7	15
0	0	28	28	11	7	13
0	0	32	36	11	5	11
0	1	32	36	14	12	11
0	1	38	40	16	12	14
0	1	32	33	12	3	15
0	1	35	37	16	11	11
0	1	32	32	13	10	15
0	0	37	38	15	12	12
0	1	34	31	16	9	14
0	1	33	37	16	12	14
0	0	33	33	14	9	8
0	0	30	30	16	12	9
0	0	24	30	14	10	15
0	0	34	31	11	9	17
0	0	34	32	12	12	13
0	1	33	34	15	8	15
0	1	34	36	15	11	15
0	1	35	37	16	11	14
0	0	35	36	16	12	16
0	0	36	33	11	10	13
0	0	34	33	15	10	16
0	1	34	33	12	12	9
0	0	41	44	12	12	16
0	0	32	39	15	11	11
0	0	30	32	15	8	10
0	1	35	35	16	12	11
0	0	28	25	14	10	15
0	1	33	35	17	11	17
0	1	39	34	14	10	14
0	0	36	35	13	8	8
0	1	36	39	15	12	15
0	0	35	33	13	12	11
0	0	38	36	14	10	16
0	1	33	32	15	12	10
0	0	31	32	12	9	15
0	1	32	36	8	6	16
0	0	31	32	14	10	19
0	0	33	34	14	9	12
0	0	34	33	11	9	8
0	0	34	35	12	9	11
0	1	34	30	13	6	14
0	0	33	38	10	10	9
0	0	32	34	16	6	15
0	1	41	33	18	14	13
0	1	34	32	13	10	16
0	0	36	31	11	10	11
0	0	37	30	4	6	12
0	0	36	27	13	12	13
0	1	29	31	16	12	10
0	0	37	30	10	7	11
0	0	27	32	12	8	12
0	0	35	35	12	11	8
0	0	28	28	10	3	12
0	0	35	33	13	6	12
0	0	29	35	12	8	11
0	0	32	35	14	9	13
0	1	36	32	10	9	14
0	1	19	21	12	8	10
0	1	21	20	12	9	12
0	0	31	34	11	7	15
0	0	33	32	10	7	13
0	1	36	34	12	6	13
0	1	33	32	16	9	13
0	0	37	33	12	10	12
0	0	34	33	14	11	12
0	0	35	37	16	12	9
0	1	31	32	14	8	9
0	1	37	34	13	11	15
0	1	35	30	4	3	10
0	1	27	30	15	11	14
0	0	34	38	11	12	15
0	0	40	36	11	7	7
0	0	29	32	14	9	14
0	0	38	34	15	12	8
0	1	34	33	14	8	10
0	0	21	27	13	11	13
0	0	36	32	11	8	13
0	1	38	34	15	10	13
0	0	30	29	11	8	8
0	0	35	35	13	7	12
0	1	30	27	13	8	13
0	1	36	33	16	10	12
0	0	34	38	13	8	10
0	1	35	36	16	12	13
0	0	34	33	16	14	12
0	0	32	39	12	7	9
0	1	33	29	7	6	15
0	0	33	32	16	11	13
0	1	26	34	5	4	13
0	0	35	38	16	9	13
0	0	21	17	4	5	15
0	0	38	35	12	9	15
0	0	35	32	15	11	14
0	1	33	34	14	12	15
0	0	37	36	11	9	11
0	0	38	31	16	12	15
0	1	34	35	15	10	14
0	0	27	29	12	9	13
0	1	16	22	6	6	12
0	0	40	41	16	10	16
0	0	36	36	10	9	16
0	1	42	42	15	13	9
0	1	30	33	14	12	14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198392&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198392&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198392&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Goodness of Fit
Correlation0.5624
R-squared0.3163
RMSE3.2245

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5624 \tabularnewline
R-squared & 0.3163 \tabularnewline
RMSE & 3.2245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198392&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5624[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3163[/C][/ROW]
[ROW][C]RMSE[/C][C]3.2245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198392&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198392&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goodness of Fit
Correlation0.5624
R-squared0.3163
RMSE3.2245







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
13838.1-0.100000000000001
23234.8988764044944-2.89887640449438
33531.16071428571433.83928571428572
43331.16071428571431.83928571428572
53733.9843.016
62933.984-4.984
73134.8988764044944-3.89887640449438
83633.9842.016
93533.9841.016
103833.9844.016
113134.8988764044944-3.89887640449438
123434.8988764044944-0.898876404494381
133534.89887640449440.101123595505619
143834.89887640449443.10112359550562
153734.89887640449442.10112359550562
163333.984-0.984000000000002
173233.984-1.984
183834.89887640449443.10112359550562
193834.89887640449443.10112359550562
203234.8988764044944-2.89887640449438
213334.8988764044944-1.89887640449438
223131.1607142857143-0.160714285714285
233834.89887640449443.10112359550562
243934.89887640449444.10112359550562
253234.8988764044944-2.89887640449438
263238.1-6.1
273534.89887640449440.101123595505619
283733.9843.016
293334.8988764044944-1.89887640449438
303333.984-0.984000000000002
313131.1607142857143-0.160714285714285
323231.16071428571430.839285714285715
333133.984-2.984
343734.89887640449442.10112359550562
353033.984-3.984
363333.984-0.984000000000002
373131.1607142857143-0.160714285714285
383333.984-0.984000000000002
393131.1607142857143-0.160714285714285
403334.8988764044944-1.89887640449438
413234.8988764044944-2.89887640449438
423333.984-0.984000000000002
433234.8988764044944-2.89887640449438
443333.984-0.984000000000002
452833.984-5.984
463533.9841.016
473933.9845.016
483433.9840.0159999999999982
493834.89887640449443.10112359550562
503233.984-1.984
513834.89887640449443.10112359550562
523033.984-3.984
533333.984-0.984000000000002
543834.89887640449443.10112359550562
553233.984-1.984
563533.9841.016
573434.8988764044944-0.898876404494381
583431.16071428571432.83928571428572
593633.9842.016
603434.8988764044944-0.898876404494381
612831.1607142857143-3.16071428571428
623434.8988764044944-0.898876404494381
633534.89887640449440.101123595505619
643531.16071428571433.83928571428572
653134.8988764044944-3.89887640449438
663734.89887640449442.10112359550562
673534.89887640449440.101123595505619
682731.1607142857143-4.16071428571428
694033.9846.016
703734.89887640449442.10112359550562
713633.9842.016
723831.16071428571436.83928571428572
733934.89887640449444.10112359550562
744134.89887640449446.10112359550562
752734.8988764044944-7.89887640449438
763034.8988764044944-4.89887640449438
773734.89887640449442.10112359550562
783134.8988764044944-3.89887640449438
793131.1607142857143-0.160714285714285
802734.8988764044944-7.89887640449438
813634.89887640449441.10112359550562
823734.89887640449442.10112359550562
833333.984-0.984000000000002
843433.9840.0159999999999982
853134.8988764044944-3.89887640449438
863933.9845.016
873434.8988764044944-0.898876404494381
883231.16071428571430.839285714285715
893333.984-0.984000000000002
903631.16071428571434.83928571428572
913234.8988764044944-2.89887640449438
924134.89887640449446.10112359550562
932833.984-5.984
943031.1607142857143-1.16071428571428
953634.89887640449441.10112359550562
963534.89887640449440.101123595505619
973134.8988764044944-3.89887640449438
983434.8988764044944-0.898876404494381
993633.9842.016
1003634.89887640449441.10112359550562
1013534.89887640449440.101123595505619
1023734.89887640449442.10112359550562
1032833.984-5.984
1043934.89887640449444.10112359550562
1053233.984-1.984
1063534.89887640449440.101123595505619
1073938.10.899999999999999
1083534.89887640449440.101123595505619
1094238.13.9
1103431.16071428571432.83928571428572
1113331.16071428571431.83928571428572
1124134.89887640449446.10112359550562
1133433.9840.0159999999999982
1143234.8988764044944-2.89887640449438
1154033.9846.016
1164033.9846.016
1173533.9841.016
1183634.89887640449441.10112359550562
1193733.9843.016
1202731.1607142857143-4.16071428571428
1213933.9845.016
1223834.89887640449443.10112359550562
1233133.984-2.984
1243331.16071428571431.83928571428572
1253233.984-1.984
1263938.10.899999999999999
1273633.9842.016
1283333.984-0.984000000000002
1293334.8988764044944-1.89887640449438
1303231.16071428571430.839285714285715
1313734.89887640449442.10112359550562
1323031.1607142857143-1.16071428571428
1333833.9844.016
1342933.984-4.984
1352231.1607142857143-9.16071428571428
1363533.9841.016
1373531.16071428571433.83928571428572
1383433.9840.0159999999999982
1393531.16071428571433.83928571428572
1403434.8988764044944-0.898876404494381
1413731.16071428571435.83928571428572
1423531.16071428571433.83928571428572
1432331.1607142857143-8.16071428571428
1443133.984-2.984
1452734.8988764044944-7.89887640449438
1463633.9842.016
1473134.8988764044944-3.89887640449438
1483233.984-1.984
1493933.9845.016
1503738.1-1.1
1513834.89887640449443.10112359550562
1523934.89887640449444.10112359550562
1533433.9840.0159999999999982
1543133.984-2.984
1553733.9843.016
1563633.9842.016
1573234.8988764044944-2.89887640449438
1583833.9844.016
1592631.1607142857143-5.16071428571428
1602631.1607142857143-5.16071428571428
1613334.8988764044944-1.89887640449438
1623933.9845.016
1633024.3755.625
1643331.16071428571431.83928571428572
1652531.1607142857143-6.16071428571428
1663833.9844.016
1673731.16071428571435.83928571428572
1683133.984-2.984
1693734.89887640449442.10112359550562
1703538.1-3.1
1712531.1607142857143-6.16071428571428
1722833.984-5.984
1733533.9841.016
1743333.984-0.984000000000002
1753031.1607142857143-1.16071428571428
1763131.1607142857143-0.160714285714285
1773733.9843.016
1783634.89887640449441.10112359550562
1793033.984-3.984
1803631.16071428571434.83928571428572
1813233.984-1.984
1822824.3753.625
1833634.89887640449441.10112359550562
1843434.8988764044944-0.898876404494381
1853133.984-2.984
1862831.1607142857143-3.16071428571428
1873633.9842.016
1883633.9842.016
1894034.89887640449445.10112359550562
1903333.984-0.984000000000002
1913734.89887640449442.10112359550562
1923233.984-1.984
1933833.9844.016
1943134.8988764044944-3.89887640449438
1953734.89887640449442.10112359550562
1963333.984-0.984000000000002
1973031.1607142857143-1.16071428571428
1983024.3755.625
1993133.984-2.984
2003233.984-1.984
2013433.9840.0159999999999982
2023633.9842.016
2033734.89887640449442.10112359550562
2043634.89887640449441.10112359550562
2053333.984-0.984000000000002
2063333.984-0.984000000000002
2073333.984-0.984000000000002
2084438.15.9
2093933.9845.016
2103231.16071428571430.839285714285715
2113534.89887640449440.101123595505619
2122531.1607142857143-6.16071428571428
2133534.89887640449440.101123595505619
2143433.9840.0159999999999982
2153533.9841.016
2163933.9845.016
2173333.984-0.984000000000002
2183633.9842.016
2193233.984-1.984
2203231.16071428571430.839285714285715
2213633.9842.016
2223231.16071428571430.839285714285715
2233433.9840.0159999999999982
2243333.984-0.984000000000002
2253533.9841.016
2263033.984-3.984
2273833.9844.016
2283434.8988764044944-0.898876404494381
2293338.1-5.1
2303233.984-1.984
2313133.984-2.984
2323033.984-3.984
2332733.984-6.984
2343131.1607142857143-0.160714285714285
2353033.984-3.984
2363231.16071428571430.839285714285715
2373533.9841.016
2382831.1607142857143-3.16071428571428
2393333.984-0.984000000000002
2403531.16071428571433.83928571428572
2413533.9841.016
2423233.984-1.984
2432124.375-3.375
2442024.375-4.375
2453431.16071428571432.83928571428572
2463233.984-1.984
2473433.9840.0159999999999982
2483234.8988764044944-2.89887640449438
2493333.984-0.984000000000002
2503333.984-0.984000000000002
2513734.89887640449442.10112359550562
2523231.16071428571430.839285714285715
2533433.9840.0159999999999982
2543033.984-3.984
2553031.1607142857143-1.16071428571428
2563833.9844.016
2573633.9842.016
2583231.16071428571430.839285714285715
2593433.9840.0159999999999982
2603333.984-0.984000000000002
2612724.3752.625
2623233.984-1.984
2633433.9840.0159999999999982
2642931.1607142857143-2.16071428571428
2653533.9841.016
2662731.1607142857143-4.16071428571428
2673334.8988764044944-1.89887640449438
2683833.9844.016
2693634.89887640449441.10112359550562
2703334.8988764044944-1.89887640449438
2713933.9845.016
2722933.984-4.984
2733234.8988764044944-2.89887640449438
2743431.16071428571432.83928571428572
2753834.89887640449443.10112359550562
2761724.375-7.375
2773533.9841.016
2783233.984-1.984
2793433.9840.0159999999999982
2803633.9842.016
2813134.8988764044944-3.89887640449438
2823533.9841.016
2832931.1607142857143-2.16071428571428
2842224.375-2.375
2854134.89887640449446.10112359550562
2863633.9842.016
2874238.13.9
2883331.16071428571431.83928571428572

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 38 & 38.1 & -0.100000000000001 \tabularnewline
2 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
3 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
4 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
5 & 37 & 33.984 & 3.016 \tabularnewline
6 & 29 & 33.984 & -4.984 \tabularnewline
7 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
8 & 36 & 33.984 & 2.016 \tabularnewline
9 & 35 & 33.984 & 1.016 \tabularnewline
10 & 38 & 33.984 & 4.016 \tabularnewline
11 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
12 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
13 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
14 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
15 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
16 & 33 & 33.984 & -0.984000000000002 \tabularnewline
17 & 32 & 33.984 & -1.984 \tabularnewline
18 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
19 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
20 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
21 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
22 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
23 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
24 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
25 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
26 & 32 & 38.1 & -6.1 \tabularnewline
27 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
28 & 37 & 33.984 & 3.016 \tabularnewline
29 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
30 & 33 & 33.984 & -0.984000000000002 \tabularnewline
31 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
32 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
33 & 31 & 33.984 & -2.984 \tabularnewline
34 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
35 & 30 & 33.984 & -3.984 \tabularnewline
36 & 33 & 33.984 & -0.984000000000002 \tabularnewline
37 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
38 & 33 & 33.984 & -0.984000000000002 \tabularnewline
39 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
40 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
41 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
42 & 33 & 33.984 & -0.984000000000002 \tabularnewline
43 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
44 & 33 & 33.984 & -0.984000000000002 \tabularnewline
45 & 28 & 33.984 & -5.984 \tabularnewline
46 & 35 & 33.984 & 1.016 \tabularnewline
47 & 39 & 33.984 & 5.016 \tabularnewline
48 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
49 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
50 & 32 & 33.984 & -1.984 \tabularnewline
51 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
52 & 30 & 33.984 & -3.984 \tabularnewline
53 & 33 & 33.984 & -0.984000000000002 \tabularnewline
54 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
55 & 32 & 33.984 & -1.984 \tabularnewline
56 & 35 & 33.984 & 1.016 \tabularnewline
57 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
58 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
59 & 36 & 33.984 & 2.016 \tabularnewline
60 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
61 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
62 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
63 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
64 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
65 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
66 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
67 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
68 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
69 & 40 & 33.984 & 6.016 \tabularnewline
70 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
71 & 36 & 33.984 & 2.016 \tabularnewline
72 & 38 & 31.1607142857143 & 6.83928571428572 \tabularnewline
73 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
74 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
75 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
76 & 30 & 34.8988764044944 & -4.89887640449438 \tabularnewline
77 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
78 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
79 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
80 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
81 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
82 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
83 & 33 & 33.984 & -0.984000000000002 \tabularnewline
84 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
85 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
86 & 39 & 33.984 & 5.016 \tabularnewline
87 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
88 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
89 & 33 & 33.984 & -0.984000000000002 \tabularnewline
90 & 36 & 31.1607142857143 & 4.83928571428572 \tabularnewline
91 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
92 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
93 & 28 & 33.984 & -5.984 \tabularnewline
94 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
95 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
96 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
97 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
98 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
99 & 36 & 33.984 & 2.016 \tabularnewline
100 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
101 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
102 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
103 & 28 & 33.984 & -5.984 \tabularnewline
104 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
105 & 32 & 33.984 & -1.984 \tabularnewline
106 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
107 & 39 & 38.1 & 0.899999999999999 \tabularnewline
108 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
109 & 42 & 38.1 & 3.9 \tabularnewline
110 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
111 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
112 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
113 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
114 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
115 & 40 & 33.984 & 6.016 \tabularnewline
116 & 40 & 33.984 & 6.016 \tabularnewline
117 & 35 & 33.984 & 1.016 \tabularnewline
118 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
119 & 37 & 33.984 & 3.016 \tabularnewline
120 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
121 & 39 & 33.984 & 5.016 \tabularnewline
122 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
123 & 31 & 33.984 & -2.984 \tabularnewline
124 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
125 & 32 & 33.984 & -1.984 \tabularnewline
126 & 39 & 38.1 & 0.899999999999999 \tabularnewline
127 & 36 & 33.984 & 2.016 \tabularnewline
128 & 33 & 33.984 & -0.984000000000002 \tabularnewline
129 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
130 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
131 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
132 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
133 & 38 & 33.984 & 4.016 \tabularnewline
134 & 29 & 33.984 & -4.984 \tabularnewline
135 & 22 & 31.1607142857143 & -9.16071428571428 \tabularnewline
136 & 35 & 33.984 & 1.016 \tabularnewline
137 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
138 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
139 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
140 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
141 & 37 & 31.1607142857143 & 5.83928571428572 \tabularnewline
142 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
143 & 23 & 31.1607142857143 & -8.16071428571428 \tabularnewline
144 & 31 & 33.984 & -2.984 \tabularnewline
145 & 27 & 34.8988764044944 & -7.89887640449438 \tabularnewline
146 & 36 & 33.984 & 2.016 \tabularnewline
147 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
148 & 32 & 33.984 & -1.984 \tabularnewline
149 & 39 & 33.984 & 5.016 \tabularnewline
150 & 37 & 38.1 & -1.1 \tabularnewline
151 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
152 & 39 & 34.8988764044944 & 4.10112359550562 \tabularnewline
153 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
154 & 31 & 33.984 & -2.984 \tabularnewline
155 & 37 & 33.984 & 3.016 \tabularnewline
156 & 36 & 33.984 & 2.016 \tabularnewline
157 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
158 & 38 & 33.984 & 4.016 \tabularnewline
159 & 26 & 31.1607142857143 & -5.16071428571428 \tabularnewline
160 & 26 & 31.1607142857143 & -5.16071428571428 \tabularnewline
161 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
162 & 39 & 33.984 & 5.016 \tabularnewline
163 & 30 & 24.375 & 5.625 \tabularnewline
164 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
165 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
166 & 38 & 33.984 & 4.016 \tabularnewline
167 & 37 & 31.1607142857143 & 5.83928571428572 \tabularnewline
168 & 31 & 33.984 & -2.984 \tabularnewline
169 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
170 & 35 & 38.1 & -3.1 \tabularnewline
171 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
172 & 28 & 33.984 & -5.984 \tabularnewline
173 & 35 & 33.984 & 1.016 \tabularnewline
174 & 33 & 33.984 & -0.984000000000002 \tabularnewline
175 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
176 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
177 & 37 & 33.984 & 3.016 \tabularnewline
178 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
179 & 30 & 33.984 & -3.984 \tabularnewline
180 & 36 & 31.1607142857143 & 4.83928571428572 \tabularnewline
181 & 32 & 33.984 & -1.984 \tabularnewline
182 & 28 & 24.375 & 3.625 \tabularnewline
183 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
184 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
185 & 31 & 33.984 & -2.984 \tabularnewline
186 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
187 & 36 & 33.984 & 2.016 \tabularnewline
188 & 36 & 33.984 & 2.016 \tabularnewline
189 & 40 & 34.8988764044944 & 5.10112359550562 \tabularnewline
190 & 33 & 33.984 & -0.984000000000002 \tabularnewline
191 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
192 & 32 & 33.984 & -1.984 \tabularnewline
193 & 38 & 33.984 & 4.016 \tabularnewline
194 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
195 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
196 & 33 & 33.984 & -0.984000000000002 \tabularnewline
197 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
198 & 30 & 24.375 & 5.625 \tabularnewline
199 & 31 & 33.984 & -2.984 \tabularnewline
200 & 32 & 33.984 & -1.984 \tabularnewline
201 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
202 & 36 & 33.984 & 2.016 \tabularnewline
203 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
204 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
205 & 33 & 33.984 & -0.984000000000002 \tabularnewline
206 & 33 & 33.984 & -0.984000000000002 \tabularnewline
207 & 33 & 33.984 & -0.984000000000002 \tabularnewline
208 & 44 & 38.1 & 5.9 \tabularnewline
209 & 39 & 33.984 & 5.016 \tabularnewline
210 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
211 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
212 & 25 & 31.1607142857143 & -6.16071428571428 \tabularnewline
213 & 35 & 34.8988764044944 & 0.101123595505619 \tabularnewline
214 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
215 & 35 & 33.984 & 1.016 \tabularnewline
216 & 39 & 33.984 & 5.016 \tabularnewline
217 & 33 & 33.984 & -0.984000000000002 \tabularnewline
218 & 36 & 33.984 & 2.016 \tabularnewline
219 & 32 & 33.984 & -1.984 \tabularnewline
220 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
221 & 36 & 33.984 & 2.016 \tabularnewline
222 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
223 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
224 & 33 & 33.984 & -0.984000000000002 \tabularnewline
225 & 35 & 33.984 & 1.016 \tabularnewline
226 & 30 & 33.984 & -3.984 \tabularnewline
227 & 38 & 33.984 & 4.016 \tabularnewline
228 & 34 & 34.8988764044944 & -0.898876404494381 \tabularnewline
229 & 33 & 38.1 & -5.1 \tabularnewline
230 & 32 & 33.984 & -1.984 \tabularnewline
231 & 31 & 33.984 & -2.984 \tabularnewline
232 & 30 & 33.984 & -3.984 \tabularnewline
233 & 27 & 33.984 & -6.984 \tabularnewline
234 & 31 & 31.1607142857143 & -0.160714285714285 \tabularnewline
235 & 30 & 33.984 & -3.984 \tabularnewline
236 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
237 & 35 & 33.984 & 1.016 \tabularnewline
238 & 28 & 31.1607142857143 & -3.16071428571428 \tabularnewline
239 & 33 & 33.984 & -0.984000000000002 \tabularnewline
240 & 35 & 31.1607142857143 & 3.83928571428572 \tabularnewline
241 & 35 & 33.984 & 1.016 \tabularnewline
242 & 32 & 33.984 & -1.984 \tabularnewline
243 & 21 & 24.375 & -3.375 \tabularnewline
244 & 20 & 24.375 & -4.375 \tabularnewline
245 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
246 & 32 & 33.984 & -1.984 \tabularnewline
247 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
248 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
249 & 33 & 33.984 & -0.984000000000002 \tabularnewline
250 & 33 & 33.984 & -0.984000000000002 \tabularnewline
251 & 37 & 34.8988764044944 & 2.10112359550562 \tabularnewline
252 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
253 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
254 & 30 & 33.984 & -3.984 \tabularnewline
255 & 30 & 31.1607142857143 & -1.16071428571428 \tabularnewline
256 & 38 & 33.984 & 4.016 \tabularnewline
257 & 36 & 33.984 & 2.016 \tabularnewline
258 & 32 & 31.1607142857143 & 0.839285714285715 \tabularnewline
259 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
260 & 33 & 33.984 & -0.984000000000002 \tabularnewline
261 & 27 & 24.375 & 2.625 \tabularnewline
262 & 32 & 33.984 & -1.984 \tabularnewline
263 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
264 & 29 & 31.1607142857143 & -2.16071428571428 \tabularnewline
265 & 35 & 33.984 & 1.016 \tabularnewline
266 & 27 & 31.1607142857143 & -4.16071428571428 \tabularnewline
267 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
268 & 38 & 33.984 & 4.016 \tabularnewline
269 & 36 & 34.8988764044944 & 1.10112359550562 \tabularnewline
270 & 33 & 34.8988764044944 & -1.89887640449438 \tabularnewline
271 & 39 & 33.984 & 5.016 \tabularnewline
272 & 29 & 33.984 & -4.984 \tabularnewline
273 & 32 & 34.8988764044944 & -2.89887640449438 \tabularnewline
274 & 34 & 31.1607142857143 & 2.83928571428572 \tabularnewline
275 & 38 & 34.8988764044944 & 3.10112359550562 \tabularnewline
276 & 17 & 24.375 & -7.375 \tabularnewline
277 & 35 & 33.984 & 1.016 \tabularnewline
278 & 32 & 33.984 & -1.984 \tabularnewline
279 & 34 & 33.984 & 0.0159999999999982 \tabularnewline
280 & 36 & 33.984 & 2.016 \tabularnewline
281 & 31 & 34.8988764044944 & -3.89887640449438 \tabularnewline
282 & 35 & 33.984 & 1.016 \tabularnewline
283 & 29 & 31.1607142857143 & -2.16071428571428 \tabularnewline
284 & 22 & 24.375 & -2.375 \tabularnewline
285 & 41 & 34.8988764044944 & 6.10112359550562 \tabularnewline
286 & 36 & 33.984 & 2.016 \tabularnewline
287 & 42 & 38.1 & 3.9 \tabularnewline
288 & 33 & 31.1607142857143 & 1.83928571428572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198392&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]38[/C][C]38.1[/C][C]-0.100000000000001[/C][/ROW]
[ROW][C]2[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]4[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]7[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]9[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]10[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]11[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]12[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]13[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]17[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]21[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]24[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]25[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]26[/C][C]32[/C][C]38.1[/C][C]-6.1[/C][/ROW]
[ROW][C]27[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]28[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]30[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]42[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]45[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]47[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]48[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]51[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]52[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]56[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]57[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]59[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]60[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]66[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]69[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]71[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]72[/C][C]38[/C][C]31.1607142857143[/C][C]6.83928571428572[/C][/ROW]
[ROW][C]73[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]76[/C][C]30[/C][C]34.8988764044944[/C][C]-4.89887640449438[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]78[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]79[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]80[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]84[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]85[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]86[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]87[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]88[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]90[/C][C]36[/C][C]31.1607142857143[/C][C]4.83928571428572[/C][/ROW]
[ROW][C]91[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]92[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]93[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]94[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]95[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]96[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]98[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]99[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]101[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]103[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]104[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]105[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]106[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]107[/C][C]39[/C][C]38.1[/C][C]0.899999999999999[/C][/ROW]
[ROW][C]108[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]109[/C][C]42[/C][C]38.1[/C][C]3.9[/C][/ROW]
[ROW][C]110[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]112[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]113[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]115[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]116[/C][C]40[/C][C]33.984[/C][C]6.016[/C][/ROW]
[ROW][C]117[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]118[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]119[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]120[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]121[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]122[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]123[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]124[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]125[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]126[/C][C]39[/C][C]38.1[/C][C]0.899999999999999[/C][/ROW]
[ROW][C]127[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]130[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]131[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]132[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]133[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]134[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]135[/C][C]22[/C][C]31.1607142857143[/C][C]-9.16071428571428[/C][/ROW]
[ROW][C]136[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]137[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]140[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]141[/C][C]37[/C][C]31.1607142857143[/C][C]5.83928571428572[/C][/ROW]
[ROW][C]142[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]143[/C][C]23[/C][C]31.1607142857143[/C][C]-8.16071428571428[/C][/ROW]
[ROW][C]144[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]145[/C][C]27[/C][C]34.8988764044944[/C][C]-7.89887640449438[/C][/ROW]
[ROW][C]146[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]148[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]149[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]150[/C][C]37[/C][C]38.1[/C][C]-1.1[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]152[/C][C]39[/C][C]34.8988764044944[/C][C]4.10112359550562[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]154[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]155[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]156[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]158[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]159[/C][C]26[/C][C]31.1607142857143[/C][C]-5.16071428571428[/C][/ROW]
[ROW][C]160[/C][C]26[/C][C]31.1607142857143[/C][C]-5.16071428571428[/C][/ROW]
[ROW][C]161[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]162[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]163[/C][C]30[/C][C]24.375[/C][C]5.625[/C][/ROW]
[ROW][C]164[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[ROW][C]165[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]166[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]167[/C][C]37[/C][C]31.1607142857143[/C][C]5.83928571428572[/C][/ROW]
[ROW][C]168[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]169[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]170[/C][C]35[/C][C]38.1[/C][C]-3.1[/C][/ROW]
[ROW][C]171[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]172[/C][C]28[/C][C]33.984[/C][C]-5.984[/C][/ROW]
[ROW][C]173[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]174[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]175[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]176[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]177[/C][C]37[/C][C]33.984[/C][C]3.016[/C][/ROW]
[ROW][C]178[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]179[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]180[/C][C]36[/C][C]31.1607142857143[/C][C]4.83928571428572[/C][/ROW]
[ROW][C]181[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]182[/C][C]28[/C][C]24.375[/C][C]3.625[/C][/ROW]
[ROW][C]183[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]184[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]185[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]186[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]187[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]188[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]189[/C][C]40[/C][C]34.8988764044944[/C][C]5.10112359550562[/C][/ROW]
[ROW][C]190[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]191[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]192[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]193[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]194[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]195[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]196[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]197[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]198[/C][C]30[/C][C]24.375[/C][C]5.625[/C][/ROW]
[ROW][C]199[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]200[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]201[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]202[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]203[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]204[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]205[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]206[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]207[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]208[/C][C]44[/C][C]38.1[/C][C]5.9[/C][/ROW]
[ROW][C]209[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]210[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]211[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]212[/C][C]25[/C][C]31.1607142857143[/C][C]-6.16071428571428[/C][/ROW]
[ROW][C]213[/C][C]35[/C][C]34.8988764044944[/C][C]0.101123595505619[/C][/ROW]
[ROW][C]214[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]215[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]216[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]217[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]218[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]219[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]220[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]221[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]222[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]223[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]224[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]225[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]226[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]227[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]228[/C][C]34[/C][C]34.8988764044944[/C][C]-0.898876404494381[/C][/ROW]
[ROW][C]229[/C][C]33[/C][C]38.1[/C][C]-5.1[/C][/ROW]
[ROW][C]230[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]231[/C][C]31[/C][C]33.984[/C][C]-2.984[/C][/ROW]
[ROW][C]232[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]233[/C][C]27[/C][C]33.984[/C][C]-6.984[/C][/ROW]
[ROW][C]234[/C][C]31[/C][C]31.1607142857143[/C][C]-0.160714285714285[/C][/ROW]
[ROW][C]235[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]236[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]237[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]238[/C][C]28[/C][C]31.1607142857143[/C][C]-3.16071428571428[/C][/ROW]
[ROW][C]239[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]240[/C][C]35[/C][C]31.1607142857143[/C][C]3.83928571428572[/C][/ROW]
[ROW][C]241[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]242[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]243[/C][C]21[/C][C]24.375[/C][C]-3.375[/C][/ROW]
[ROW][C]244[/C][C]20[/C][C]24.375[/C][C]-4.375[/C][/ROW]
[ROW][C]245[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]246[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]247[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]248[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]249[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]250[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]251[/C][C]37[/C][C]34.8988764044944[/C][C]2.10112359550562[/C][/ROW]
[ROW][C]252[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]253[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]254[/C][C]30[/C][C]33.984[/C][C]-3.984[/C][/ROW]
[ROW][C]255[/C][C]30[/C][C]31.1607142857143[/C][C]-1.16071428571428[/C][/ROW]
[ROW][C]256[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]257[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]258[/C][C]32[/C][C]31.1607142857143[/C][C]0.839285714285715[/C][/ROW]
[ROW][C]259[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]260[/C][C]33[/C][C]33.984[/C][C]-0.984000000000002[/C][/ROW]
[ROW][C]261[/C][C]27[/C][C]24.375[/C][C]2.625[/C][/ROW]
[ROW][C]262[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]263[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]264[/C][C]29[/C][C]31.1607142857143[/C][C]-2.16071428571428[/C][/ROW]
[ROW][C]265[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]266[/C][C]27[/C][C]31.1607142857143[/C][C]-4.16071428571428[/C][/ROW]
[ROW][C]267[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]268[/C][C]38[/C][C]33.984[/C][C]4.016[/C][/ROW]
[ROW][C]269[/C][C]36[/C][C]34.8988764044944[/C][C]1.10112359550562[/C][/ROW]
[ROW][C]270[/C][C]33[/C][C]34.8988764044944[/C][C]-1.89887640449438[/C][/ROW]
[ROW][C]271[/C][C]39[/C][C]33.984[/C][C]5.016[/C][/ROW]
[ROW][C]272[/C][C]29[/C][C]33.984[/C][C]-4.984[/C][/ROW]
[ROW][C]273[/C][C]32[/C][C]34.8988764044944[/C][C]-2.89887640449438[/C][/ROW]
[ROW][C]274[/C][C]34[/C][C]31.1607142857143[/C][C]2.83928571428572[/C][/ROW]
[ROW][C]275[/C][C]38[/C][C]34.8988764044944[/C][C]3.10112359550562[/C][/ROW]
[ROW][C]276[/C][C]17[/C][C]24.375[/C][C]-7.375[/C][/ROW]
[ROW][C]277[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]278[/C][C]32[/C][C]33.984[/C][C]-1.984[/C][/ROW]
[ROW][C]279[/C][C]34[/C][C]33.984[/C][C]0.0159999999999982[/C][/ROW]
[ROW][C]280[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]281[/C][C]31[/C][C]34.8988764044944[/C][C]-3.89887640449438[/C][/ROW]
[ROW][C]282[/C][C]35[/C][C]33.984[/C][C]1.016[/C][/ROW]
[ROW][C]283[/C][C]29[/C][C]31.1607142857143[/C][C]-2.16071428571428[/C][/ROW]
[ROW][C]284[/C][C]22[/C][C]24.375[/C][C]-2.375[/C][/ROW]
[ROW][C]285[/C][C]41[/C][C]34.8988764044944[/C][C]6.10112359550562[/C][/ROW]
[ROW][C]286[/C][C]36[/C][C]33.984[/C][C]2.016[/C][/ROW]
[ROW][C]287[/C][C]42[/C][C]38.1[/C][C]3.9[/C][/ROW]
[ROW][C]288[/C][C]33[/C][C]31.1607142857143[/C][C]1.83928571428572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198392&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198392&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
13838.1-0.100000000000001
23234.8988764044944-2.89887640449438
33531.16071428571433.83928571428572
43331.16071428571431.83928571428572
53733.9843.016
62933.984-4.984
73134.8988764044944-3.89887640449438
83633.9842.016
93533.9841.016
103833.9844.016
113134.8988764044944-3.89887640449438
123434.8988764044944-0.898876404494381
133534.89887640449440.101123595505619
143834.89887640449443.10112359550562
153734.89887640449442.10112359550562
163333.984-0.984000000000002
173233.984-1.984
183834.89887640449443.10112359550562
193834.89887640449443.10112359550562
203234.8988764044944-2.89887640449438
213334.8988764044944-1.89887640449438
223131.1607142857143-0.160714285714285
233834.89887640449443.10112359550562
243934.89887640449444.10112359550562
253234.8988764044944-2.89887640449438
263238.1-6.1
273534.89887640449440.101123595505619
283733.9843.016
293334.8988764044944-1.89887640449438
303333.984-0.984000000000002
313131.1607142857143-0.160714285714285
323231.16071428571430.839285714285715
333133.984-2.984
343734.89887640449442.10112359550562
353033.984-3.984
363333.984-0.984000000000002
373131.1607142857143-0.160714285714285
383333.984-0.984000000000002
393131.1607142857143-0.160714285714285
403334.8988764044944-1.89887640449438
413234.8988764044944-2.89887640449438
423333.984-0.984000000000002
433234.8988764044944-2.89887640449438
443333.984-0.984000000000002
452833.984-5.984
463533.9841.016
473933.9845.016
483433.9840.0159999999999982
493834.89887640449443.10112359550562
503233.984-1.984
513834.89887640449443.10112359550562
523033.984-3.984
533333.984-0.984000000000002
543834.89887640449443.10112359550562
553233.984-1.984
563533.9841.016
573434.8988764044944-0.898876404494381
583431.16071428571432.83928571428572
593633.9842.016
603434.8988764044944-0.898876404494381
612831.1607142857143-3.16071428571428
623434.8988764044944-0.898876404494381
633534.89887640449440.101123595505619
643531.16071428571433.83928571428572
653134.8988764044944-3.89887640449438
663734.89887640449442.10112359550562
673534.89887640449440.101123595505619
682731.1607142857143-4.16071428571428
694033.9846.016
703734.89887640449442.10112359550562
713633.9842.016
723831.16071428571436.83928571428572
733934.89887640449444.10112359550562
744134.89887640449446.10112359550562
752734.8988764044944-7.89887640449438
763034.8988764044944-4.89887640449438
773734.89887640449442.10112359550562
783134.8988764044944-3.89887640449438
793131.1607142857143-0.160714285714285
802734.8988764044944-7.89887640449438
813634.89887640449441.10112359550562
823734.89887640449442.10112359550562
833333.984-0.984000000000002
843433.9840.0159999999999982
853134.8988764044944-3.89887640449438
863933.9845.016
873434.8988764044944-0.898876404494381
883231.16071428571430.839285714285715
893333.984-0.984000000000002
903631.16071428571434.83928571428572
913234.8988764044944-2.89887640449438
924134.89887640449446.10112359550562
932833.984-5.984
943031.1607142857143-1.16071428571428
953634.89887640449441.10112359550562
963534.89887640449440.101123595505619
973134.8988764044944-3.89887640449438
983434.8988764044944-0.898876404494381
993633.9842.016
1003634.89887640449441.10112359550562
1013534.89887640449440.101123595505619
1023734.89887640449442.10112359550562
1032833.984-5.984
1043934.89887640449444.10112359550562
1053233.984-1.984
1063534.89887640449440.101123595505619
1073938.10.899999999999999
1083534.89887640449440.101123595505619
1094238.13.9
1103431.16071428571432.83928571428572
1113331.16071428571431.83928571428572
1124134.89887640449446.10112359550562
1133433.9840.0159999999999982
1143234.8988764044944-2.89887640449438
1154033.9846.016
1164033.9846.016
1173533.9841.016
1183634.89887640449441.10112359550562
1193733.9843.016
1202731.1607142857143-4.16071428571428
1213933.9845.016
1223834.89887640449443.10112359550562
1233133.984-2.984
1243331.16071428571431.83928571428572
1253233.984-1.984
1263938.10.899999999999999
1273633.9842.016
1283333.984-0.984000000000002
1293334.8988764044944-1.89887640449438
1303231.16071428571430.839285714285715
1313734.89887640449442.10112359550562
1323031.1607142857143-1.16071428571428
1333833.9844.016
1342933.984-4.984
1352231.1607142857143-9.16071428571428
1363533.9841.016
1373531.16071428571433.83928571428572
1383433.9840.0159999999999982
1393531.16071428571433.83928571428572
1403434.8988764044944-0.898876404494381
1413731.16071428571435.83928571428572
1423531.16071428571433.83928571428572
1432331.1607142857143-8.16071428571428
1443133.984-2.984
1452734.8988764044944-7.89887640449438
1463633.9842.016
1473134.8988764044944-3.89887640449438
1483233.984-1.984
1493933.9845.016
1503738.1-1.1
1513834.89887640449443.10112359550562
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2883331.16071428571431.83928571428572



Parameters (Session):
par1 = 5 ; par2 = none ; par3 = 10 ; par4 = no ;
Parameters (R input):
par1 = 5 ; par2 = none ; par3 = 10 ; par4 = no ;
R code (references can be found in the software module):
par4 <- 'no'
par3 <- '10'
par2 <- 'none'
par1 <- '4'
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}